diff --git a/Analysis/0109_preprocess/0116_preprocess_8.2.ipynb b/Analysis/0109_preprocess/0116_preprocess_8.2.ipynb index fd5dfe53a..42a97ce4a 100644 --- a/Analysis/0109_preprocess/0116_preprocess_8.2.ipynb +++ b/Analysis/0109_preprocess/0116_preprocess_8.2.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -24,14 +24,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 17280/17280 [00:13<00:00, 1299.31it/s]\n" + "100%|██████████| 17280/17280 [00:18<00:00, 924.90it/s] \n" ] }, { @@ -161,7 +161,7 @@ "9 177 2 2 7 3" ] }, - "execution_count": 4, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -177,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -296,7 +296,7 @@ "5 176 1 B 4" ] }, - "execution_count": 5, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -317,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -396,210 +396,1192 @@ " 서\n", " \n", " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", + " 5\n", + " 175\n", + " 3\n", + " B\n", + " 1\n", + " 동\n", + " 남\n", " \n", " \n", - " 59\n", - " 210\n", + " 6\n", + " 175\n", + " 4\n", + " A\n", + " 5\n", + " 서\n", + " 북\n", + " \n", + " \n", + " 7\n", + " 175\n", + " 4\n", + " B\n", + " 2\n", + " 서\n", + " 동\n", + " \n", + " \n", + " 8\n", + " 176\n", + " 1\n", + " A\n", + " 8\n", + " 남\n", + " 북\n", + " \n", + " \n", + " 9\n", + " 176\n", + " 1\n", + " B\n", " 4\n", + " 북\n", + " 남\n", + " \n", + " \n", + " 10\n", + " 176\n", + " 2\n", + " A\n", + " 8\n", + " 남\n", + " 북\n", + " \n", + " \n", + " 11\n", + " 176\n", + " 2\n", " B\n", " 3\n", " 남\n", " 서\n", " \n", " \n", - " 60\n", - " 211\n", - " 1\n", + " 12\n", + " 176\n", + " 3\n", " A\n", - " 6\n", - " 동\n", + " 5\n", " 서\n", + " 북\n", " \n", " \n", - " 61\n", - " 211\n", + " 13\n", + " 176\n", + " 3\n", + " B\n", + " 18\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 14\n", + " 177\n", + " 1\n", + " A\n", + " 8\n", + " 남\n", + " 북\n", + " \n", + " \n", + " 15\n", + " 177\n", " 1\n", " B\n", + " 4\n", + " 북\n", + " 남\n", + " \n", + " \n", + " 16\n", + " 177\n", " 2\n", - " 서\n", + " A\n", + " 7\n", + " 북\n", " 동\n", " \n", " \n", - " 62\n", - " 211\n", + " 17\n", + " 177\n", " 2\n", + " B\n", + " 3\n", + " 남\n", + " 서\n", + " \n", + " \n", + " 18\n", + " 177\n", + " 3\n", " A\n", " 17\n", " NaN\n", " NaN\n", " \n", " \n", - " 63\n", - " 211\n", - " 2\n", + " 19\n", + " 177\n", + " 3\n", " B\n", " 18\n", " NaN\n", " NaN\n", " \n", - " \n", - "\n", - "

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" \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -609,10 +1591,32 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -620,30 +1624,51 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - 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"execution_count": 7, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -693,7 +1765,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -757,7 +1829,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -1651,46 +2723,6 @@ " 571511538_01\n", " i6\n", " \n", - " \n", - " 86\n", - " 211\n", - " 2\n", - " 서\n", - " 동\n", - " 571542116_02.96\n", - " 571542116_02.164\n", - " c30\n", - " \n", - " \n", - " 87\n", - " 211\n", - " 6\n", - " 동\n", - " 서\n", - " 571542116_01\n", - " -571542116_02.96\n", - " c30\n", - " \n", - " \n", - " 88\n", - " 211\n", - " 17\n", - " None\n", - " None\n", - " None\n", - " None\n", - " c30\n", - " \n", - " \n", - " 89\n", - " 211\n", - " 18\n", - " None\n", - " None\n", - " None\n", - " None\n", - " c30\n", - " \n", " \n", "\n", "" @@ -1783,10 +2815,6 @@ "83 210 21 서 남 571500535_02.18 571500585_01 \n", "84 210 21 남 동 571500585_02 571542115_01 \n", "85 210 21 동 북 -571542115_01 571511538_01 \n", - "86 211 2 서 동 571542116_02.96 571542116_02.164 \n", - "87 211 6 동 서 571542116_01 -571542116_02.96 \n", - "88 211 17 None None None None \n", - "89 211 18 None None None None \n", "\n", " node_id \n", "0 i0 \n", @@ -1874,11 +2902,7 @@ "82 i6 \n", "83 i6 \n", "84 i6 \n", - "85 i6 \n", - "86 c30 \n", - "87 c30 \n", - "88 c30 \n", - "89 c30 " + "85 i6 " ] }, "metadata": {}, @@ -1961,21 +2985,21 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/17280 [00:00 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(m, \u001b[38;5;28mlen\u001b[39m(time2movement[\u001b[43mfmins\u001b[49m[\u001b[38;5;241m287\u001b[39m]]))\n", + "\u001b[1;31mNameError\u001b[0m: name 'fmins' is not defined" ] } ], @@ -2346,7 +3088,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2635,7 +3377,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -3059,7 +3801,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -3163,7 +3905,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -3183,7 +3925,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -3260,7 +4002,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -3660,7 +4402,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -3695,7 +4437,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -5731,7 +6473,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -5978,7 +6720,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -6071,7 +6813,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -14748,7 +15490,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -15083,7 +15825,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -15103,7 +15845,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -15359,7 +16101,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -15368,7 +16110,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -15724,7 +16466,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, "outputs": [ { diff --git a/Analysis/0109_preprocess/0116_preprocess_8.ipynb b/Analysis/0109_preprocess/0116_preprocess_8.ipynb index c1c0aa392..22ffbc062 100644 --- a/Analysis/0109_preprocess/0116_preprocess_8.ipynb +++ b/Analysis/0109_preprocess/0116_preprocess_8.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 324, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -24,21 +24,14 @@ }, { "cell_type": "code", - "execution_count": 325, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 17/17280 [00:00<01:44, 165.78it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 17280/17280 [01:02<00:00, 277.24it/s]\n" + "100%|██████████| 17280/17280 [00:14<00:00, 1222.96it/s]\n" ] }, { @@ -168,7 +161,7 @@ "9 177 2 2 7 3" ] }, - "execution_count": 325, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -184,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 326, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -303,7 +296,7 @@ "5 176 1 B 4" ] }, - "execution_count": 326, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -324,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 327, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -965,7 +958,7 @@ "59 210 4 B 3 남 서" ] }, - "execution_count": 327, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -979,7 +972,7 @@ }, { "cell_type": "code", - "execution_count": 328, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -1742,7 +1735,7 @@ "59 210 4 B 3 남 서 180 270" ] }, - "execution_count": 328, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -1772,7 +1765,7 @@ }, { "cell_type": "code", - "execution_count": 329, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -1948,7 +1941,7 @@ "1 571510153_02 571545870_01 i0 " ] }, - "execution_count": 329, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -2015,7 +2008,7 @@ }, { "cell_type": "code", - "execution_count": 394, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -3602,7 +3595,7 @@ }, { "cell_type": "code", - "execution_count": 398, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -3829,7 +3822,7 @@ "[116 rows x 11 columns]" ] }, - "execution_count": 398, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -3840,7 +3833,7 @@ }, { "cell_type": "code", - "execution_count": 414, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -3877,7 +3870,7 @@ " \n", " 0\n", " 175\n", - " 1.0\n", + " 1\n", " 동\n", " 남\n", " 571545870_02\n", @@ -3887,7 +3880,7 @@ " \n", " 1\n", " 175\n", - " 2.0\n", + " 2\n", " 서\n", " 동\n", " 571510153_02\n", @@ -3897,7 +3890,7 @@ " \n", " 2\n", " 175\n", - " 3.0\n", + " 3\n", " 남\n", " 서\n", " -571542797_02\n", @@ -3907,7 +3900,7 @@ " \n", " 3\n", " 175\n", - " 4.0\n", + " 4\n", " 북\n", " 남\n", " -571500487_01\n", @@ -3917,7 +3910,7 @@ " \n", " 4\n", " 175\n", - " 5.0\n", + " 5\n", " 서\n", " 북\n", " 571510153_02\n", @@ -3937,7 +3930,7 @@ " \n", " 69\n", " 210\n", - " 8.0\n", + " 8\n", " 남\n", " 북\n", " 571500585_02\n", @@ -3947,7 +3940,7 @@ " \n", " 70\n", " 210\n", - " 21.0\n", + " 21\n", " 북\n", " 서\n", " 571511538_02.121\n", @@ -3957,7 +3950,7 @@ " \n", " 71\n", " 210\n", - " 21.0\n", + " 21\n", " 서\n", " 남\n", " 571500535_02.18\n", @@ -3967,7 +3960,7 @@ " \n", " 72\n", " 210\n", - " 21.0\n", + " 21\n", " 남\n", " 동\n", " 571500585_02\n", @@ -3977,7 +3970,7 @@ " \n", " 73\n", " 210\n", - " 21.0\n", + " 21\n", " 동\n", " 북\n", " -571542115_01\n", @@ -3991,17 +3984,17 @@ ], "text/plain": [ " inter_no move_no inc_dir out_dir inc_edge out_edge node_id\n", - "0 175 1.0 동 남 571545870_02 571542797_02 i0\n", - "1 175 2.0 서 동 571510153_02 571545870_01 i0\n", - "2 175 3.0 남 서 -571542797_02 571510153_01 i0\n", - "3 175 4.0 북 남 -571500487_01 571542797_02 i0\n", - "4 175 5.0 서 북 571510153_02 571500487_01 i0\n", + "0 175 1 동 남 571545870_02 571542797_02 i0\n", + "1 175 2 서 동 571510153_02 571545870_01 i0\n", + "2 175 3 남 서 -571542797_02 571510153_01 i0\n", + "3 175 4 북 남 -571500487_01 571542797_02 i0\n", + "4 175 5 서 북 571510153_02 571500487_01 i0\n", ".. ... ... ... ... ... ... ...\n", - "69 210 8.0 남 북 571500585_02 571511538_01 i6\n", - "70 210 21.0 북 서 571511538_02.121 571500535_01 i6\n", - "71 210 21.0 서 남 571500535_02.18 571500585_01 i6\n", - "72 210 21.0 남 동 571500585_02 571542115_01 i6\n", - "73 210 21.0 동 북 -571542115_01 571511538_01 i6\n", + "69 210 8 남 북 571500585_02 571511538_01 i6\n", + "70 210 21 북 서 571511538_02.121 571500535_01 i6\n", + "71 210 21 서 남 571500535_02.18 571500585_01 i6\n", + "72 210 21 남 동 571500585_02 571542115_01 i6\n", + "73 210 21 동 북 -571542115_01 571511538_01 i6\n", "\n", "[74 rows x 7 columns]" ] @@ -4078,6 +4071,7 @@ "matching.append(match7[match7.node_id.isin(child_ids)])\n", "matching = pd.concat(matching)\n", "matching = matching.dropna().sort_values(by=['inter_no', 'node_id', 'move_no']).reset_index(drop=True)\n", + "matching['move_no'] = matching['move_no'].astype(int)\n", "display(matching)" ] }, @@ -4090,16 +4084,83 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 11, "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "1704380565\n" + "100%|██████████| 17280/17280 [02:16<00:00, 126.94it/s]\n" ] - }, + } + ], + "source": [ + "# 5초 단위로 이동류번호 저장 및 신호이력에서 유닉스시각 가져와서 표시, 한시간동안의 데이터만 보관\n", + "midnight = int(datetime(2024, 1, 5, 0, 0, 0).timestamp())\n", + "next_day = int(datetime(2024, 1, 6, 0, 0, 0).timestamp())\n", + "fsecs = range(midnight, next_day, 5) # fsecs : unix time by Five SECondS\n", + "fmins = range(midnight, next_day, 300) # fmins : unix time by Five MINuteS\n", + "# time2move = dict(zip(fsecs,moves)) # move : 어느 순간의 이동류정보\n", + "history = pd.read_csv('../../Data/tables/history.csv', index_col=0)\n", + "\n", + "time2movement = {} # movement : 어느 순간의, 그 순간으로부터 한시간 동안의 (교차로번호 + 현시별이동류번호 + 시작시간)\n", + "# - 아래 절차를 5초마다 반복\n", + "for fsec in tqdm(fsecs): # fsec : unix time by Five SECond\n", + " # 1. 상태 테이블 조회해서 전체 데이터중 필요데이터(교차로번호, A링 현시번호, A링 이동류번호, B링 현시번호, B링 이동류번호)만 수집 : A\n", + " # move = time2move[fsec]\n", + " move = pd.read_csv(f'../../Data/tables/moves/move_{fsec}.csv', index_col=0)\n", + " # 2. 이력 테이블 조회해서 교차로별로 유닉스시간 최대인 데이터(교차로변호, 종료유닉스타임)만 수집 : B\n", + " recent_histories = [group.iloc[-1:] for _, group in history[history['end_unix'] < fsec].groupby('inter_no')] # 교차로별로 유닉스시간이 최대인 행들\n", + " if not recent_histories:\n", + " rhistory = pd.DataFrame({'inter_no':[], 'end_unix':[]}) # recent history\n", + " else:\n", + " rhistory = pd.concat(recent_histories)\n", + " recent_unix = rhistory[['inter_no', 'end_unix']]\n", + " # 3. 상태 테이블 조회정보(A)와 이력 테이블 조회정보(B) 조인(키값 : 교차로번호) : C\n", + " move = pd.merge(move, recent_unix, how='left', on='inter_no')\n", + " move['end_unix'] = move['end_unix'].fillna(0).astype(int)\n", + " move = move.drop_duplicates()\n", + " # 4. C데이터 프레임에 신규 컬럼(시작 유닉스타임) 생성 후 종료유닉스 타임 값 입력, 종료 유닉스 타임 컬럼 제거\n", + " move = move.rename(columns = {'end_unix':'start_unix'})\n", + " # 5. 이동류 이력정보 READ\n", + " # - CSV 파일로 서버에 저장된 이동류정보를 읽어옴(파일이 없는 경우에는 데이터가 없는 프레임 D 생성)\n", + " try:\n", + " if isinstance(movement, pd.DataFrame): # movement가 존재할 경우 그걸 그대로 씀.\n", + " pass\n", + " else: \n", + " movement = pd.DataFrame()\n", + " except NameError: # movement가 존재하지 않는 경우 생성\n", + " movement = pd.DataFrame()\n", + " # 6. 이동류 이력정보 데이터테이블(D)에 C데이터 add\n", + " movement = pd.concat([movement, move])\n", + " # 7. D데이터 프레임에서 중복데이터 제거(교차로번호, 시작 유닉스타임, A링 현시번호, B링 현시번호 같은 행은 제거)\n", + " movement = movement.drop_duplicates(['inter_no','phas_A','phas_B','start_unix'])\n", + " # 8. D데이터 보관 시간 기준시간을 시작 유닉스 타임의 최대값 - 3600을 값으로 산출하고, 보관 시간 기준시간보다 작은 시작 유닉스 타임을 가진 행은 모두 제거(1시간 데이터만 보관)\n", + " movement = movement[movement.start_unix > fsec - 3600]\n", + " movement = movement.sort_values(by=['start_unix','inter_no','phas_A','phas_B']).reset_index(drop=True)\n", + "\n", + " time2movement[fsec] = movement\n", + " movement.to_csv(f'../../Data/tables/movements/movements_{fsec}.csv')\n", + "\n", + "# 각 movement들의 길이 시각화\n", + "import matplotlib.pyplot as plt\n", + "plt.plot(fsecs, [len(time2movement[fsec]) for fsec in fsecs])\n", + "plt.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# C. 5분 간격으로 신호이력 수집 및 통합테이블 생성" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ { "data": { "text/html": [ @@ -4122,1686 +4183,123 @@ " \n", " \n", " inter_no\n", - " phas_A\n", - " phas_B\n", - " move_A\n", - " move_B\n", - " start_unix\n", + " end_unix\n", + " dura_A1\n", + " dura_A2\n", + " dura_A3\n", + " dura_A4\n", + " dura_A5\n", + " dura_A6\n", + " dura_A7\n", + " dura_A8\n", + " dura_B1\n", + " dura_B2\n", + " dura_B3\n", + " dura_B4\n", + " dura_B5\n", + " dura_B6\n", + " dura_B7\n", + " dura_B8\n", + " cycle\n", + " offset\n", " \n", " \n", " \n", " \n", " 0\n", - " 175\n", - " 1\n", - " 1\n", - " 8\n", - " 4\n", - " 1704380560\n", + " 206\n", + " 1704380520\n", + " 33\n", + " 35\n", + " 26\n", + " 26\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 33\n", + " 35\n", + " 26\n", + " 26\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 120\n", + " 10\n", " \n", " \n", " 1\n", - " 176\n", - " 1\n", - " 1\n", - " 8\n", - " 4\n", - " 1704380550\n", - " \n", - " \n", - " 2\n", - " 177\n", - " 1\n", - " 1\n", - " 8\n", - " 4\n", - " 1704380550\n", - " \n", - " \n", - " 3\n", " 178\n", - " 1\n", - " 1\n", - " 8\n", - " 4\n", " 1704380540\n", + " 38\n", + " 39\n", + " 40\n", + " 23\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 38\n", + " 39\n", + " 40\n", + " 23\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 140\n", + " 50\n", " \n", " \n", - " 4\n", + " 2\n", " 201\n", - " 2\n", - " 2\n", - " 5\n", - " 2\n", " 1704380540\n", + " 24\n", + " 24\n", + " 17\n", + " 58\n", + " 17\n", + " 0\n", + " 0\n", + " 0\n", + " 24\n", + " 24\n", + " 17\n", + " 58\n", + " 17\n", + " 0\n", + " 0\n", + " 0\n", + " 140\n", + " 133\n", " \n", " \n", - " 5\n", + " 3\n", " 202\n", - " 1\n", - " 1\n", - " 6\n", - " 2\n", - " 1704380541\n", - " \n", - " \n", - " 6\n", - " 206\n", - " 2\n", - " 2\n", - " 17\n", - " 18\n", - " 1704380520\n", + " 1704380540\n", + " 39\n", + " 101\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 39\n", + " 101\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 140\n", + " 103\n", " \n", " \n", - " 7\n", - " 210\n", - " 1\n", - " 1\n", - " 6\n", - " 18\n", - " 1704380550\n", - " \n", - " \n", - "\n", - "" - ], - "text/plain": [ - " inter_no phas_A phas_B move_A move_B start_unix\n", - "0 175 1 1 8 4 1704380560\n", - "1 176 1 1 8 4 1704380550\n", - "2 177 1 1 8 4 1704380550\n", - "3 178 1 1 8 4 1704380540\n", - "4 201 2 2 5 2 1704380540\n", - "5 202 1 1 6 2 1704380541\n", - "6 206 2 2 17 18 1704380520\n", - "7 210 1 1 6 18 1704380550" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " inter_no phas_A phas_B move_A move_B start_unix\n", - "0 206 2 2 17 18 1704380520\n", - "1 206 2 2 17 18 1704380520\n", - "2 206 2 2 17 18 1704380520\n", - "3 178 1 1 8 4 1704380540\n", - "4 178 1 1 8 4 1704380540\n", - "5 178 1 1 8 4 1704380540\n", - "6 201 2 2 5 2 1704380540\n", - "7 201 2 2 5 2 1704380540\n", - "8 201 2 2 5 2 1704380540\n", - "9 202 1 1 6 2 1704380541\n", - "10 202 1 1 6 2 1704380541\n", - "11 202 1 1 6 2 1704380541\n", - "12 176 1 1 8 4 1704380550\n", - "13 176 1 1 8 4 1704380550\n", - "14 176 1 1 8 4 1704380550\n", - "15 177 1 1 8 4 1704380550\n", - "16 177 1 1 8 4 1704380550\n", - "17 177 1 1 8 4 1704380550\n", - "18 210 1 1 6 18 1704380550\n", - "19 210 1 1 6 18 1704380550\n", - "20 210 1 2 6 2 1704380550\n", - "21 175 1 1 8 4 1704380560\n", - "22 175 1 1 8 4 1704380560\n", - "23 175 1 1 8 4 1704380560" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "del movement\n", - "fsec = fsecs[33]\n", - "print(fsec)\n", - "move = pd.read_csv(f'../../Data/tables/moves/move_{fsec}.csv', index_col=0)\n", - "recent_histories = [group.iloc[-1:] for _, group in history[history['end_unix'] < fsec].groupby('inter_no')] # 교차로별로 유닉스시간이 최대인 행들\n", - "if not recent_histories:\n", - " rhistory = pd.DataFrame({'inter_no':[], 'end_unix':[]}) # recent history\n", - "else:\n", - " rhistory = pd.concat(recent_histories)\n", - "recent_unix = rhistory[['inter_no', 'end_unix']]\n", - "move = pd.merge(move, recent_unix, how='left', on='inter_no')\n", - "move['end_unix'] = move['end_unix'].fillna(0).astype(int)\n", - "move = move.drop_duplicates()\n", - "move = move.rename(columns = {'end_unix':'start_unix'})\n", - "display(move)\n", - "try:\n", - " if isinstance(movement, pd.DataFrame): # movement가 존재할 경우 그걸 그대로 씀.\n", - " pass\n", - " else: \n", - " movement = pd.DataFrame()\n", - "except NameError: # movement가 존재하지 않는 경우 생성\n", - " movement = pd.DataFrame()\n", - "movement = pd.concat([movement, move])\n", - "movement = movement[movement.start_unix > fsec - 3600]\n", - "movement = movement.sort_values(by=['start_unix','inter_no','phas_A','phas_B']).reset_index(drop=True)\n", - "display(movement)\n", - "\n", - "fsec = fsecs[34]\n", - "print(fsec)\n", - "move = pd.read_csv(f'../../Data/tables/moves/move_{fsec}.csv', index_col=0)\n", - "recent_histories = [group.iloc[-1:] for _, group in history[history['end_unix'] < fsec].groupby('inter_no')] # 교차로별로 유닉스시간이 최대인 행들\n", - "if not recent_histories:\n", - " rhistory = pd.DataFrame({'inter_no':[], 'end_unix':[]}) # recent history\n", - "else:\n", - " rhistory = pd.concat(recent_histories)\n", - "recent_unix = rhistory[['inter_no', 'end_unix']]\n", - "move = pd.merge(move, recent_unix, how='left', on='inter_no')\n", - "move['end_unix'] = move['end_unix'].fillna(0).astype(int)\n", - "move = move.drop_duplicates()\n", - "move = move.rename(columns = {'end_unix':'start_unix'})\n", - "display(move)\n", - "try:\n", - " if isinstance(movement, pd.DataFrame): # movement가 존재할 경우 그걸 그대로 씀.\n", - " pass\n", - " else: \n", - " movement = pd.DataFrame()\n", - "except NameError: # movement가 존재하지 않는 경우 생성\n", - " movement = pd.DataFrame()\n", - "display(movement)\n", - "movement = pd.concat([movement, move])\n", - "movement = movement[movement.start_unix > fsec - 3600]\n", - "movement = movement.sort_values(by=['start_unix','inter_no','phas_A','phas_B']).reset_index(drop=True)\n", - "\n", - "fsec = fsecs[35]\n", - "print(fsec)\n", - "move = pd.read_csv(f'../../Data/tables/moves/move_{fsec}.csv', index_col=0)\n", - "recent_histories = [group.iloc[-1:] for _, group in history[history['end_unix'] < fsec].groupby('inter_no')] # 교차로별로 유닉스시간이 최대인 행들\n", - "if not recent_histories:\n", - " rhistory = pd.DataFrame({'inter_no':[], 'end_unix':[]}) # recent history\n", - "else:\n", - " rhistory = pd.concat(recent_histories)\n", - "recent_unix = rhistory[['inter_no', 'end_unix']]\n", - "move = pd.merge(move, recent_unix, how='left', on='inter_no')\n", - "move['end_unix'] = move['end_unix'].fillna(0).astype(int)\n", - "move = move.drop_duplicates()\n", - "move = move.rename(columns = {'end_unix':'start_unix'})\n", - "display(move)\n", - "try:\n", - " if isinstance(movement, pd.DataFrame): # movement가 존재할 경우 그걸 그대로 씀.\n", - " pass\n", - " else: \n", - " movement = pd.DataFrame()\n", - "except NameError: # movement가 존재하지 않는 경우 생성\n", - " movement = pd.DataFrame()\n", - "display(movement)\n", - "movement = pd.concat([movement, move])\n", - "movement = movement[movement.start_unix > fsec - 3600]\n", - "movement = movement.sort_values(by=['start_unix','inter_no','phas_A','phas_B']).reset_index(drop=True)\n", - "\n", - "fsec = fsecs[36]\n", - "print(fsec)\n", - "move = pd.read_csv(f'../../Data/tables/moves/move_{fsec}.csv', index_col=0)\n", - "recent_histories = [group.iloc[-1:] for _, group in history[history['end_unix'] < fsec].groupby('inter_no')] # 교차로별로 유닉스시간이 최대인 행들\n", - "if not recent_histories:\n", - " rhistory = pd.DataFrame({'inter_no':[], 'end_unix':[]}) # recent history\n", - "else:\n", - " rhistory = pd.concat(recent_histories)\n", - "recent_unix = rhistory[['inter_no', 'end_unix']]\n", - "move = pd.merge(move, recent_unix, how='left', on='inter_no')\n", - "move['end_unix'] = move['end_unix'].fillna(0).astype(int)\n", - "move = move.drop_duplicates()\n", - "move = move.rename(columns = {'end_unix':'start_unix'})\n", - "display(move)\n", - "try:\n", - " if isinstance(movement, pd.DataFrame): # movement가 존재할 경우 그걸 그대로 씀.\n", - " pass\n", - " else: \n", - " movement = pd.DataFrame()\n", - "except NameError: # movement가 존재하지 않는 경우 생성\n", - " movement = pd.DataFrame()\n", - "display(movement)\n", - "movement = pd.concat([movement, move])\n", - "movement = movement[movement.start_unix > fsec - 3600]\n", - "movement = movement.sort_values(by=['start_unix','inter_no','phas_A','phas_B']).reset_index(drop=True)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 415, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/17280 [00:00 fsec - 3600]\n", - " movement = movement.sort_values(by=['start_unix','inter_no','phas_A','phas_B']).reset_index(drop=True)\n", - "\n", - " time2movement[fsec] = movement\n", - " movement.to_csv(f'../../Data/tables/movements/movements_{fsec}.csv')\n", - "\n", - "# 각 movement들의 길이 시각화\n", - "import matplotlib.pyplot as plt\n", - "plt.plot(fsecs, [len(time2movement[fsec]) for fsec in fsecs])\n", - "plt.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# C. 5분 간격으로 신호이력 수집 및 통합테이블 생성" - ] - }, - { - "cell_type": "code", - "execution_count": 416, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " inter_no start_hour start_minute ddur_1 ddur_2 ddur_3 ddur_4 ddur_5 \\\n", - "0 175 0 0 37 39 25 30 29 \n", - "1 175 7 0 40 42 29 26 33 \n", - "2 175 9 0 43 45 33 22 37 \n", - "3 175 18 30 46 48 37 18 41 \n", - "4 176 0 0 37 73 40 0 0 \n", - "\n", - " ddur_6 ddur_7 ddur_8 cycle offset \n", - "0 0 0 0 160 57 \n", - "1 0 0 0 170 40 \n", - "2 0 0 0 180 28 \n", - "3 0 0 0 190 18 \n", - "4 0 0 0 150 131 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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"output_type": "stream", - "text": [ - "2024-01-05 14:29:20\n", - "2024-01-05 15:29:19\n" - ] - } - ], - "source": [ - "# inter_no = 175, m = 30 : 조정 (수축)\n", - "# inter_no = 175, m = 70 : 삭제 + 조정(수축)\n", - "# inter_no = 175, m = 90 : 결측(전이) + 삭제 + 조정(수축)\n", - "# inter_no = 175, m = 140 : 삭제 + 조정(수축)\n", - "# inter_no = 176, m = 50 : 조정(수축)\n", - "# inter_no = 176, m = 155 : 삭제(마지막 행에서 삭제)\n", - "# inter_no = 176, m = 160 : 조정(수축) + 삭제 + 조정(수축)\n", - "# inter_no = 176, m = 190 : 결측\n", - "# inter_no = 176, m = 220 : 삭제\n", - "# inter_no = 176, m = 270 : 삭제\n", - "# inter_no = 176, m = 275 : 삭제\n", - "# inter_no = 177, m = 40 : 조정(수축)\n", - "# inter_no = 178, m = 70 : 삭제\n", - "# inter_no = 178, m = 100 : 조정(확장) + 삭제\n", - "# inter_no = 178, m = 270 : 결측 + 조정(확장)\n", - "\n", - "print(datetime.fromtimestamp(1704432560))\n", - "print(datetime.fromtimestamp(1704436159))" - ] - }, - { - "cell_type": "code", - "execution_count": 419, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -6015,17 +4478,17 @@ " output : rhistory\n", " - recent history\n", " - 현재시각(present_time) 이전 1시간 동안의 신호이력에 대하여 결측치 및 이상치를 처리한 결과\n", - " - 교차로번호(inter_no), 종료유닉스(end_unix), 현시시간(ddur_j), 주기(cycle), 옵셋(offset)\n", + " - 교차로번호(inter_no), 종료유닉스(end_unix), 현시시간(dur_Aj, dur_Bj), 주기(cycle), 옵셋(offset)\n", " '''\n", " fmins = range(midnight, next_day, 300) # fmins : unix time by Five MINuteS\n", " present_time = fmins[m] # 현재시점\n", " print(datetime.fromtimestamp(present_time))\n", "\n", - " Rhists = []\n", + " Rhists = [] # Recent history (1시간 이내)\n", " for inter_no in history.inter_no.unique():\n", " # - 5분마다 신호이력 데이터 수집해서 통합테이블 생성할때\n", " # 1. 조회시점의 유닉스 타임을 기준으로 신호이력의 유닉스 타임이 1시간 이내인(Rhist) 데이터 수집\n", - " rhistory = history.copy() # recent history (3시간 이내)\n", + " rhistory = history.copy() # recent history (3시간 이내 : 1시간 이내의 이력에 대한 전처리를 위해 3시간 이내 데이터가 필요함)\n", " rhistory = rhistory[(rhistory.end_unix < present_time) & (rhistory.end_unix >= present_time - 10800)]\n", " hours = np.array(range(midnight, next_day + 1, 3600))\n", " rhist = rhistory.copy()[rhistory.inter_no == inter_no] # 특정한 inter_no\n", @@ -6045,12 +4508,11 @@ " idx = (start_seconds <= curr_unix).sum() - 1\n", " start_hour = timetable.iloc[idx].start_hour\n", " start_minute = timetable.iloc[idx].start_minute\n", - " prow = pland[(pland.inter_no==inter_no) & (pland.start_hour==start_hour) & (pland.start_minute==start_minute)] # planned row\n", + " prow = plan[(plan.inter_no==inter_no) & (plan.start_hour==start_hour) & (plan.start_minute==start_minute)] # planned row\n", " prow = prow.drop(['start_hour', 'start_minute'], axis=1)\n", " prow['end_unix'] = curr_unix\n", " cycle = prow.iloc[0].cycle\n", " new_rows.append(prow)\n", - "\n", " rhist = pd.concat([rhist] + new_rows).sort_values(['end_unix'])\n", " rhist = rhist.reset_index(drop=True)\n", "\n", @@ -6069,9 +4531,9 @@ " base_unix = end_unixes_lt_ghour[-5] # 기준유닉스 : curr_unix보다 작은 hour 중에서 가장 큰 값으로부터 다섯 번째로 작은 end_unix\n", " # D_n : 시간차이\n", " D_n = curr_unix - base_unix\n", - " ddurations = rhist[(rhist.end_unix > base_unix) & (rhist.end_unix <= curr_unix)][[f'ddur_{j}' for j in range(1,9)]]\n", + " ddurations = rhist[(rhist.end_unix > base_unix) & (rhist.end_unix <= curr_unix)][[f'dura_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)]]\n", " # S_n : 현시시간합\n", - " S_n = ddurations.values.sum()\n", + " S_n = ddurations.values.sum() // 2\n", " Rhist.loc[n, ['D_n', 'S_n']] = [D_n, S_n]\n", " n = 1\n", " while n < len(Rhist):\n", @@ -6085,8 +4547,8 @@ " # D_n : 시간차이\n", " D_n = curr_unix - base_unix\n", " # S_n : 현시시간합\n", - " ddurations = rhist[(rhist.end_unix > base_unix) & (rhist.end_unix <= curr_unix)][[f'ddur_{j}' for j in range(1,9)]]\n", - " S_n = ddurations.values.sum()\n", + " ddurations = rhist[(rhist.end_unix > base_unix) & (rhist.end_unix <= curr_unix)][[f'dura_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)]]\n", + " S_n = ddurations.values.sum() // 2\n", " # 비율이 0.5보다 작거나 같으면 해당 행을 삭제\n", " if (abs(D_n - S_n) > 10) & (R_n <= 0.5):\n", " # print(\"lt\", inter_no, curr_unix, round(R_n,2), D_n, S_n)\n", @@ -6104,16 +4566,16 @@ " # D_n : 시간차이\n", " D_n = curr_unix - base_unix\n", " # S_n : 현시시간합\n", - " ddurations = rhist[(rhist.end_unix > base_unix) & (rhist.end_unix <= curr_unix)][[f'ddur_{j}' for j in range(1,9)]]\n", - " S_n = ddurations.values.sum()\n", + " ddurations = rhist[(rhist.end_unix > base_unix) & (rhist.end_unix <= curr_unix)][[f'dura_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)]]\n", + " S_n = ddurations.values.sum() // 2\n", " # 비율이 0.5보다 크면 해당 행 조정 (비율을 유지한 채로 현시시간 대체)\n", " if (abs(D_n - S_n) > 10) & (R_n > 0.5):\n", " start_seconds = np.array(timetable.start_seconds)\n", " idx = (start_seconds <= curr_unix).sum() - 1\n", " start_hour = timetable.iloc[idx].start_hour\n", " start_minute = timetable.iloc[idx].start_minute\n", - " prow = pland[(pland.inter_no==inter_no) & (pland.start_hour==start_hour) & (pland.start_minute==start_minute)].copy().reset_index(drop=True).iloc[0] # planned row\n", - " adjusted_dur = prow[['ddur_1', 'ddur_2', 'ddur_3', 'ddur_4', 'ddur_5', 'ddur_6', 'ddur_7', 'ddur_8']] * R_n\n", + " prow = plan[(plan.inter_no==inter_no) & (plan.start_hour==start_hour) & (plan.start_minute==start_minute)].copy().reset_index(drop=True).iloc[0] # planned row\n", + " adjusted_dur = prow[[f'dura_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)]] * R_n\n", " # 조정된 현시시간을 정수로 바꿈\n", " int_parts = adjusted_dur.apply(lambda x: int(x))\n", " frac_parts = adjusted_dur - int_parts\n", @@ -6123,8 +4585,8 @@ " max_frac_index = frac_parts.idxmax()\n", " int_parts[max_frac_index] += 1\n", " frac_parts[max_frac_index] = 0 # 이미 처리된 항목은 0으로 설정\n", - " Rhist.loc[n, ['ddur_1', 'ddur_2', 'ddur_3', 'ddur_4', 'ddur_5', 'ddur_6', 'ddur_7', 'ddur_8']] = int_parts.values\n", - " Rhist.loc[n, 'cycle'] = int_parts.sum()\n", + " Rhist.loc[n, [f'dura_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)]] = int_parts.values\n", + " Rhist.loc[n, 'cycle'] = int_parts.sum() // 2\n", " # print(\"gt\", inter_no, curr_unix, round(R_n,2), D_n, S_n)\n", " n += 1\n", " Rhist = Rhist.drop(columns=['offset', 'D_n', 'S_n'])\n", @@ -6136,7 +4598,7 @@ }, { "cell_type": "code", - "execution_count": 420, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -6152,7 +4614,7 @@ " # 이전 유닉스 존재, abs >=10 => 현시시간 합의 차\n", " inter_no = row.inter_no\n", " end_unix = row.end_unix\n", - " elapsed_time = row[[f'ddur_{j}' for j in range(1, 9)]].sum()\n", + " elapsed_time = row[[f'dura_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)]].sum() // 2 # 현시시간 합\n", " start_unix = end_unix - elapsed_time\n", " pre_rows = history[:i] # previous rows\n", " if inter_no in pre_rows.inter_no.unique(): # 이전 유닉스 존재\n", @@ -6166,30 +4628,35 @@ " rhistory['start_unix'] = rhistory['start_unix'].astype(int)\n", " # # with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n", " # # display(rhistory)\n", - " rhistory = rhistory[['inter_no', 'start_unix'] + [f'ddur_{j}' for j in range(1, 9)] + ['cycle']]\n", + " rhistory = rhistory[['inter_no', 'start_unix'] + [f'dura_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)] + ['cycle']]\n", + "\n", " # 계층화된 형태로 변환\n", " hrhistory = [] # hierarchied recent history\n", " for i, row in rhistory.iterrows():\n", - " inter_no = row.inter_no #\n", - " start_unix = row.start_unix #\n", + " inter_no = row.inter_no\n", + " start_unix = row.start_unix\n", + "\n", " ind = (timetable['start_seconds'] <= row.start_unix).sum() - 1\n", " start_hour = timetable.iloc[ind].start_hour\n", " start_minute = timetable.iloc[ind].start_minute\n", " isplit = isplits[(inter_no, start_hour, start_minute)]\n", - " new_rows = []\n", - " for j in isplit.keys():\n", - " phas_A, phas_B = isplit[j]\n", - " duration = row[f'ddur_{j}']\n", - " new_rows.append(pd.DataFrame({'inter_no':[inter_no], 'start_unix':[start_unix],\n", - " 'phas_A':[phas_A],'phas_B':[phas_B],'duration':[duration]}))\n", - " new_rows = pd.concat(new_rows)\n", + " phas_As = [isplit[j][0] for j in isplit.keys()]\n", + " phas_Bs = [isplit[j][1] for j in isplit.keys()]\n", + " durs_A = row[[f'dura_A{j}' for j in range(1,9)]]\n", + " durs_B = row[[f'dura_B{j}' for j in range(1,9)]]\n", + " cums_A = durs_A.cumsum()\n", + " cums_B = durs_B.cumsum()\n", + " cycle = durs_A.sum()\n", + " cums = np.unique(np.concatenate((cums_A,cums_B)))\n", + " durations = np.concatenate(([cums[0]], np.diff(cums)))\n", + " new_rows = pd.DataFrame({'inter_no':[inter_no] * len(durations), 'start_unix':[start_unix] * len(durations),\n", + " 'phas_A':phas_As, 'phas_B':phas_Bs, 'duration':durations})\n", " hrhistory.append(new_rows)\n", " hrhistory = pd.concat(hrhistory)\n", " hrhistory = hrhistory.sort_values(by = ['start_unix', 'inter_no', 'phas_A', 'phas_B']).reset_index(drop=True)\n", "\n", " # 5초단위로 수집한 이동류정보(time2movement[present_time])와 최근 1시간 신호이력(hrhistory)을 병합\n", - " movedur = pd.merge(time2movement[present_time], hrhistory, how='inner', on=['inter_no', 'start_unix', 'phas_A', 'phas_B'])\n", - " # movements and durations\n", + " movedur = pd.merge(time2movement[present_time], hrhistory, how='inner', on=['inter_no', 'start_unix', 'phas_A', 'phas_B']) # movements and durations\n", " movedur = movedur.sort_values(by=['start_unix', 'inter_no', 'phas_A','phas_B'])\n", " movedur = movedur[['inter_no', 'start_unix', 'phas_A', 'phas_B', 'move_A', 'move_B', 'duration']]\n", " # 이동류 매칭 테이블에서 진입id, 진출id를 가져와서 붙임.\n", @@ -6198,19 +4665,247 @@ " start_unix = row.start_unix\n", " move_A = row.move_A\n", " move_B = row.move_B\n", - " match_A = matching[(matching.inter_no == inter_no) & (matching.move_no == move_A)].iloc[0]\n", - " match_B = matching[(matching.inter_no == inter_no) & (matching.move_no == move_B)].iloc[0]\n", - " inc_edge_A = match_A.inc_edge\n", - " inc_edge_B = match_B.inc_edge\n", - " out_edge_A = match_A.out_edge\n", - " out_edge_B = match_B.out_edge\n", + " if move_A in [17, 18]:\n", + " inc_edge_A = np.nan\n", + " out_edge_A = np.nan\n", + " elif move_B in [17, 18]:\n", + " inc_edge_B = np.nan\n", + " out_edge_B = np.nan\n", + " else:\n", + " match_A = matching[(matching.inter_no == inter_no) & (matching.move_no == move_A)].iloc[0]\n", + " match_B = matching[(matching.inter_no == inter_no) & (matching.move_no == move_B)].iloc[0]\n", + " inc_edge_A = match_A.inc_edge\n", + " inc_edge_B = match_B.inc_edge\n", + " out_edge_A = match_A.out_edge\n", + " out_edge_B = match_B.out_edge\n", " movedur.loc[i, ['inc_edge_A', 'inc_edge_B', 'out_edge_A', 'out_edge_B']] = [inc_edge_A, inc_edge_B, out_edge_A, out_edge_B]\n", " # 이동류 컬럼 제거\n", - 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" + ], + "text/plain": [ + " inter_no start_unix phas_A phas_B duration inc_edge_A \\\n", + "0 175 1704385840 1 1 37 -571542797_02 \n", + "1 175 1704385840 2 2 39 -571500487_01 \n", + "2 175 1704385840 3 3 25 571545870_02 \n", + "3 175 1704385840 3 4 30 571545870_02 \n", + "4 175 1704385840 4 4 29 571510153_02 \n", + ".. ... ... ... ... ... ... \n", + "582 210 1704389240 1 1 24 NaN \n", + "583 210 1704389240 1 2 19 -571542115_01 \n", + "584 210 1704389240 2 2 29 571500535_02.18 \n", + "585 210 1704389240 3 3 56 571511538_02.121 \n", + "586 210 1704389240 4 4 22 571500585_02 \n", + "\n", + " inc_edge_B out_edge_A out_edge_B \n", + "0 -571500487_01 571500487_01 571542797_02 \n", + "1 -571542797_02 571545870_01 571510153_01 \n", + "2 571545870_02 571510153_01 571542797_02 \n", + "3 571510153_02 571510153_01 571545870_01 \n", + "4 571510153_02 571500487_01 571545870_01 \n", + ".. ... ... ... \n", + "582 NaN NaN NaN \n", + "583 571500535_02.18 571500535_01 571542115_01 \n", + "584 571500535_02.18 571511538_01 571542115_01 \n", + "585 571511538_02.121 571542115_01 571500585_01 \n", + "586 571500585_02 571511538_01 571500535_01 \n", + "\n", + "[587 rows x 9 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "make_histid(30)" + ] } ], "metadata": { diff --git a/Analysis/0117_make_signals/0117_make_signals.ipynb b/Analysis/0117_make_signals/0117_make_signals.ipynb index 2bc8b6a70..da224c492 100644 --- a/Analysis/0117_make_signals/0117_make_signals.ipynb +++ b/Analysis/0117_make_signals/0117_make_signals.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 12, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -17,14 +17,14 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "1704406800\n" + "1704410400\n" ] }, { @@ -64,73 +64,73 @@ " \n", " \n", " 0\n", - " i9\n", - " 1704403210\n", + " i1\n", + " 1704406840\n", " 1\n", " 1\n", - " 39\n", - " 571510152_02\n", - " 571510152_01\n", - " -571510152_01\n", - " 571510152_01.65\n", - " 6\n", - " 2\n", + " 37\n", + " -571542810_01\n", + " 571542797_02.99\n", + " -571542797_02.99\n", + " 571542810_01\n", + " 8\n", + " 4\n", " \n", " \n", " 1\n", - " i9\n", - " 1704403210\n", + " i1\n", + " 1704406840\n", " 2\n", " 2\n", - " 101\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " 17\n", - " 18\n", + " 93\n", + " -571542810_01\n", + " -571542810_01\n", + " -571542797_02.99\n", + " 571543469_01\n", + " 8\n", + " 3\n", " \n", " \n", " 2\n", - " i6\n", - " 1704403210\n", - " 1\n", - " 1\n", - " 24\n", - " -571542115_01\n", + " i1\n", + " 1704406840\n", + " 3\n", + " 3\n", + " 40\n", + " 571543469_02\n", " NaN\n", - " 571500535_01\n", + " -571542797_02.99\n", " NaN\n", - " 6\n", + " 5\n", " 18\n", " \n", " \n", " 3\n", - " i6\n", - " 1704403210\n", + " i9\n", + " 1704406850\n", " 1\n", - " 2\n", - " 19\n", - " -571542115_01\n", - " 571500535_02.18\n", - " 571500535_01\n", - " 571542115_01\n", + " 1\n", + " 46\n", + " 571510152_02\n", + " 571510152_01\n", + " -571510152_01\n", + " 571510152_01.65\n", " 6\n", " 2\n", " \n", " \n", " 4\n", - " i6\n", - " 1704403210\n", - " 2\n", + " i9\n", + " 1704406850\n", " 2\n", - " 29\n", - " 571500535_02.18\n", - " 571500535_02.18\n", - " 571511538_01\n", - " 571542115_01\n", - " 5\n", " 2\n", + " 114\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 17\n", + " 18\n", " \n", " \n", " ...\n", @@ -147,68 +147,54 @@ " ...\n", " \n", " \n", - " 417\n", - " i8\n", - " 1704406590\n", - " 5\n", - " 5\n", - " 18\n", - " 571500583_01\n", - " 571500583_01\n", - " 571500617_01\n", - " 571500569_01\n", - " 7\n", + " 477\n", + " i6\n", + " 1704410200\n", " 4\n", + " 4\n", + " 23\n", + " 571500585_02\n", + " 571500585_02\n", + " 571511538_01\n", + " 571500535_01\n", + " 8\n", + " 3\n", " \n", " \n", - " 418\n", - " i7\n", - " 1704406630\n", + " 478\n", + " i2\n", + " 1704410220\n", " 1\n", " 1\n", - " 44\n", - " -571511538_02\n", - " 571542073_01\n", - " 571542073_02\n", - " 571511538_02\n", + " 40\n", + " -571542809_01\n", + " 571542811_02\n", + " 571542811_01\n", + " 571542809_01\n", " 8\n", " 4\n", " \n", " \n", - " 419\n", - " i7\n", - " 1704406630\n", + " 479\n", + " i2\n", + " 1704410220\n", " 2\n", " 2\n", - " 44\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " 17\n", - " 18\n", + " 25\n", + " 571542811_02\n", + " -571542809_01\n", + " 571542107_01\n", + " 571542809_01\n", + " 7\n", + " 3\n", " \n", " \n", - " 420\n", - " i7\n", - " 1704406630\n", + " 480\n", + " i2\n", + " 1704410220\n", " 3\n", " 3\n", - " 26\n", - " -571511538_02\n", - " 571542073_01\n", - " 571542073_02\n", - " 571511538_02\n", - " 8\n", - " 4\n", - " \n", - " \n", - " 421\n", - " i7\n", - " 1704406630\n", - " 4\n", - " 4\n", - " 26\n", + " 71\n", " NaN\n", " NaN\n", " NaN\n", @@ -216,42 +202,56 @@ " 17\n", " 18\n", " \n", + " \n", + " 481\n", + " i2\n", + " 1704410220\n", + " 4\n", + " 4\n", + " 34\n", + " -571542809_01\n", + " 571542107_02\n", + " 571542811_01\n", + " 571542809_01\n", + " 5\n", + " 1\n", + " \n", " \n", "\n", - "

422 rows × 11 columns

\n", + "

482 rows × 11 columns

\n", "" ], "text/plain": [ - " node_id start_unix phas_A phas_B duration inc_edge_A \\\n", - "0 i9 1704403210 1 1 39 571510152_02 \n", - "1 i9 1704403210 2 2 101 NaN \n", - "2 i6 1704403210 1 1 24 -571542115_01 \n", - "3 i6 1704403210 1 2 19 -571542115_01 \n", - "4 i6 1704403210 2 2 29 571500535_02.18 \n", - ".. ... ... ... ... ... ... \n", - "417 i8 1704406590 5 5 18 571500583_01 \n", - "418 i7 1704406630 1 1 44 -571511538_02 \n", - "419 i7 1704406630 2 2 44 NaN \n", - "420 i7 1704406630 3 3 26 -571511538_02 \n", - "421 i7 1704406630 4 4 26 NaN \n", + " node_id start_unix phas_A phas_B duration inc_edge_A \\\n", + "0 i1 1704406840 1 1 37 -571542810_01 \n", + "1 i1 1704406840 2 2 93 -571542810_01 \n", + "2 i1 1704406840 3 3 40 571543469_02 \n", + "3 i9 1704406850 1 1 46 571510152_02 \n", + "4 i9 1704406850 2 2 114 NaN \n", + ".. ... ... ... ... ... ... \n", + "477 i6 1704410200 4 4 23 571500585_02 \n", + "478 i2 1704410220 1 1 40 -571542809_01 \n", + "479 i2 1704410220 2 2 25 571542811_02 \n", + "480 i2 1704410220 3 3 71 NaN \n", + "481 i2 1704410220 4 4 34 -571542809_01 \n", "\n", - " inc_edge_B out_edge_A out_edge_B move_A move_B \n", - "0 571510152_01 -571510152_01 571510152_01.65 6 2 \n", - "1 NaN NaN NaN 17 18 \n", - "2 NaN 571500535_01 NaN 6 18 \n", - "3 571500535_02.18 571500535_01 571542115_01 6 2 \n", - "4 571500535_02.18 571511538_01 571542115_01 5 2 \n", - ".. ... ... ... ... ... \n", - "417 571500583_01 571500617_01 571500569_01 7 4 \n", - "418 571542073_01 571542073_02 571511538_02 8 4 \n", - "419 NaN NaN NaN 17 18 \n", - "420 571542073_01 571542073_02 571511538_02 8 4 \n", - "421 NaN NaN NaN 17 18 \n", + " inc_edge_B out_edge_A out_edge_B move_A move_B \n", + "0 571542797_02.99 -571542797_02.99 571542810_01 8 4 \n", + "1 -571542810_01 -571542797_02.99 571543469_01 8 3 \n", + "2 NaN -571542797_02.99 NaN 5 18 \n", + "3 571510152_01 -571510152_01 571510152_01.65 6 2 \n", + "4 NaN NaN NaN 17 18 \n", + ".. ... ... ... ... ... \n", + "477 571500585_02 571511538_01 571500535_01 8 3 \n", + "478 571542811_02 571542811_01 571542809_01 8 4 \n", + "479 -571542809_01 571542107_01 571542809_01 7 3 \n", + "480 NaN NaN NaN 17 18 \n", + "481 571542107_02 571542811_01 571542809_01 5 1 \n", "\n", - "[422 rows x 11 columns]" + "[482 rows x 11 columns]" ] }, - "execution_count": 13, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -260,7 +260,7 @@ "midnight = int(datetime(2024, 1, 5, 0, 0, 0).timestamp())\n", "next_day = int(datetime(2024, 1, 6, 0, 0, 0).timestamp())\n", "fmins = range(midnight, next_day, 300)\n", - "m = 88 # m ranges from 26 to 287\n", + "m = 100 # m ranges from 26 to 287\n", "print(fmins[m])\n", "net = sumolib.net.readNet('../../Data/networks/SN_sample.net.xml')\n", "inter_node = pd.read_csv('../../data/tables/inter_node.csv', index_col=0)\n", @@ -276,7 +276,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -298,7 +298,141 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "i0 grrrgrrrgrrrrrgrr\n", + "i1 grrrrrrgrr\n", + "i2 rrggrrr\n", + "i3 grrrrgrrrrgrrrrgrrrr\n", + "i6 grrrgrrrrgrrgrrr\n", + "i7 rrrggrr\n", + "i8 grrrrrrrgrrrgrrr\n", + "i9 rrrr\n" + ] + } + ], + "source": [ + "node2init = {}\n", + "for node in nodes:\n", + " node_id = node.getID()\n", + " conns = [(c.getJunctionIndex(), c) for c in node.getConnections()]\n", + " conns = [c for c in conns if c[0] >= 0]\n", + " conns = sorted(conns, key=lambda x: x[0])\n", + " state = []\n", + " for i, ci in conns:\n", + " if ci.getTLLinkIndex() < 0:\n", + " continue\n", + " are_foes = False\n", + " for j, cj in conns:\n", + " if ci.getTo() == cj.getTo():\n", + " continue\n", + " if node.areFoes(i, j):\n", + " are_foes = True\n", + " break\n", + " state.append('r' if are_foes else 'g')\n", + " node2init[node_id] = state\n", + "\n", + "# 어떤 연결과도 상충이 일어나지는 않지만, 신호가 부여되어 있는 경우에는 r을 부여\n", + "for _, row in sigtable.iterrows():\n", + " node_id = row['node_id']\n", + " inc_edge_A = row.inc_edge_A\n", + " inc_edge_B = row.inc_edge_B\n", + " out_edge_A = row.out_edge_A\n", + " out_edge_B = row.out_edge_B\n", + " move_A = row['move_A']\n", + " move_B = row['move_B']\n", + "\n", + " if pd.isna(inc_edge_A) or pd.isna(out_edge_A):\n", + " continue\n", + " else:\n", + " inc_edge_A = net.getEdge(inc_edge_A)\n", + " out_edge_A = net.getEdge(out_edge_A)\n", + " for conn in inc_edge_A.getConnections(out_edge_A):\n", + " index = conn.getTLLinkIndex()\n", + " if index >= 0:\n", + " node2init[node_id][index] = 'r'\n", + "\n", + " if pd.isna(inc_edge_B) or pd.isna(out_edge_B):\n", + " continue\n", + " else:\n", + " inc_edge_B = net.getEdge(inc_edge_B)\n", + " out_edge_B = net.getEdge(out_edge_B)\n", + " for conn in inc_edge_B.getConnections(out_edge_B):\n", + " index = conn.getTLLinkIndex()\n", + " if index >= 0:\n", + " node2init[node_id][index] = 'r'\n", + "for node_id in node_ids:\n", + " print(node_id, \"\".join(node2init[node_id]))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['g', 'r', 'r', 'r', 'g', 'r', 'r', 'r', 'r', 'g', 'r', 'r', 'g', 'r', 'r', 'r']\n", + "['G', 'r', 'r', 'r', 'g', 'r', 'r', 'r', 'r', 'g', 'r', 'r', 'g', 'r', 'r', 'r']\n", + "['g', 'r', 'r', 'r', 'g', 'r', 'r', 'r', 'r', 'g', 'r', 'r', 'g', 'r', 'r', 'r']\n" + ] + } + ], + "source": [ + "print(node2init['i6'])\n", + "state = copy.deepcopy(node2init)['i6']\n", + "state[0] = 'G'\n", + "print(state)\n", + "print(node2init['i6'])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 1., 7., 11., 12., 6., 7., 3., 1., 2., 3.]),\n", + " array([ 0. , 12.2, 24.4, 36.6, 48.8, 61. , 73.2, 85.4, 97.6,\n", + " 109.8, 122. ]),\n", + " )" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plan = pd.read_csv('../../Data/tables/plan.csv', index_col=0)\n", + "durs = plan[[f'dura_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)]].values.flatten()\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "plt.hist(np.unique(durs))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -324,266 +458,169 @@ " \n", " node_id\n", " start_unix\n", - " phas_A\n", - " phas_B\n", + " phase_sumo\n", " duration\n", - " inc_edge_A\n", - " inc_edge_B\n", - " out_edge_A\n", - " out_edge_B\n", - " move_A\n", - " move_B\n", + " state\n", + " start_dt\n", " \n", " \n", " \n", " \n", - " 0\n", - " i9\n", - " 1704403210\n", - " 1\n", + " 399\n", + " i7\n", + " 1704410130\n", + " 0\n", + " 44\n", + " GGrggGG\n", + " 2024-01-05 08:15:30\n", + " \n", + " \n", + " 400\n", + " i7\n", + " 1704410130\n", " 1\n", - " 39\n", - " 571510152_02\n", - " 571510152_01\n", - " -571510152_01\n", - " 571510152_01.65\n", - " 6\n", - " 2\n", + " 26\n", + " GGrggGG\n", + " 2024-01-05 08:15:30\n", " \n", " \n", - " 1\n", - " i9\n", - " 1704403210\n", - " 2\n", - " 2\n", - " 101\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " 17\n", - " 18\n", - " \n", - " \n", - " 2\n", - " i6\n", - " 1704403210\n", - " 1\n", - " 1\n", - " 24\n", - " -571542115_01\n", - " NaN\n", - " 571500535_01\n", - " NaN\n", - " 6\n", - " 18\n", + " 401\n", + " i0\n", + " 1704410170\n", + " 0\n", + " 40\n", + " gGGrgrrrgGGGGrgrr\n", + " 2024-01-05 08:16:10\n", " \n", " \n", - " 3\n", - " i6\n", - " 1704403210\n", + " 402\n", + " i0\n", + " 1704410170\n", " 1\n", - " 2\n", - " 19\n", - " -571542115_01\n", - " 571500535_02.18\n", - " 571500535_01\n", - " 571542115_01\n", - " 6\n", - " 2\n", + " 42\n", + " grrGgrrrgrrrrGgrr\n", + " 2024-01-05 08:16:10\n", " \n", " \n", - " 4\n", - " i6\n", - " 1704403210\n", - " 2\n", + " 403\n", + " i0\n", + " 1704410170\n", " 2\n", " 29\n", - " 571500535_02.18\n", - " 571500535_02.18\n", - " 571511538_01\n", - " 571542115_01\n", - " 5\n", - " 2\n", + " grrrgGGGgrrrrrgrr\n", + " 2024-01-05 08:16:10\n", " \n", " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", + " 404\n", + " i0\n", + " 1704410170\n", + " 3\n", + " 26\n", + " grrrgGGrgrrrrrgGr\n", + " 2024-01-05 08:16:10\n", " \n", " \n", - " 417\n", - " i8\n", - " 1704406590\n", - " 5\n", - " 5\n", - " 18\n", - " 571500583_01\n", - " 571500583_01\n", - " 571500617_01\n", - " 571500569_01\n", - " 7\n", + " 405\n", + " i0\n", + " 1704410170\n", " 4\n", + " 33\n", + " grrrgrrrgrrrrrgGG\n", + " 2024-01-05 08:16:10\n", " \n", " \n", - " 418\n", - " i7\n", - " 1704406630\n", - " 1\n", + " 406\n", + " i6\n", + " 1704410200\n", + " 0\n", + " 24\n", + " grrrgGGGrgrrgrrr\n", + " 2024-01-05 08:16:40\n", + " \n", + " \n", + " 407\n", + " i6\n", + " 1704410200\n", " 1\n", - " 44\n", - " -571511538_02\n", - " 571542073_01\n", - " 571542073_02\n", - " 571511538_02\n", - " 8\n", - " 4\n", + " 19\n", + " grrrgGGGrgrrgGGr\n", + " 2024-01-05 08:16:40\n", " \n", " \n", - " 419\n", - " i7\n", - " 1704406630\n", - " 2\n", + " 408\n", + " i6\n", + " 1704410200\n", " 2\n", - " 44\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " 17\n", - " 18\n", + " 39\n", + " grrrgrrrrgrrgGGG\n", + " 2024-01-05 08:16:40\n", " \n", " \n", - " 420\n", - " i7\n", - " 1704406630\n", - " 3\n", + " 409\n", + " i6\n", + " 1704410200\n", " 3\n", - " 26\n", - " -571511538_02\n", - " 571542073_01\n", - " 571542073_02\n", - " 571511538_02\n", - " 8\n", - " 4\n", + " 65\n", + " gGGGgrrrrgrrgrrr\n", + " 2024-01-05 08:16:40\n", " \n", " \n", - " 421\n", - " i7\n", - " 1704406630\n", - " 4\n", + " 410\n", + " i6\n", + " 1704410200\n", " 4\n", - " 26\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " 17\n", - " 18\n", + " 23\n", + " grrrgrrrrgGGgrrr\n", + " 2024-01-05 08:16:40\n", " \n", " \n", "\n", - "

422 rows × 11 columns

\n", "" ], "text/plain": [ - " node_id start_unix phas_A phas_B duration inc_edge_A \\\n", - "0 i9 1704403210 1 1 39 571510152_02 \n", - "1 i9 1704403210 2 2 101 NaN \n", - "2 i6 1704403210 1 1 24 -571542115_01 \n", - "3 i6 1704403210 1 2 19 -571542115_01 \n", - "4 i6 1704403210 2 2 29 571500535_02.18 \n", - ".. ... ... ... ... ... ... \n", - "417 i8 1704406590 5 5 18 571500583_01 \n", - "418 i7 1704406630 1 1 44 -571511538_02 \n", - "419 i7 1704406630 2 2 44 NaN \n", - "420 i7 1704406630 3 3 26 -571511538_02 \n", - "421 i7 1704406630 4 4 26 NaN \n", - "\n", - " inc_edge_B out_edge_A out_edge_B move_A move_B \n", - "0 571510152_01 -571510152_01 571510152_01.65 6 2 \n", - "1 NaN NaN NaN 17 18 \n", - "2 NaN 571500535_01 NaN 6 18 \n", - "3 571500535_02.18 571500535_01 571542115_01 6 2 \n", - "4 571500535_02.18 571511538_01 571542115_01 5 2 \n", - ".. ... ... ... ... ... \n", - "417 571500583_01 571500617_01 571500569_01 7 4 \n", - "418 571542073_01 571542073_02 571511538_02 8 4 \n", - "419 NaN NaN NaN 17 18 \n", - "420 571542073_01 571542073_02 571511538_02 8 4 \n", - "421 NaN NaN NaN 17 18 \n", + " node_id start_unix phase_sumo duration state \\\n", + "399 i7 1704410130 0 44 GGrggGG \n", + "400 i7 1704410130 1 26 GGrggGG \n", + "401 i0 1704410170 0 40 gGGrgrrrgGGGGrgrr \n", + "402 i0 1704410170 1 42 grrGgrrrgrrrrGgrr \n", + "403 i0 1704410170 2 29 grrrgGGGgrrrrrgrr \n", + "404 i0 1704410170 3 26 grrrgGGrgrrrrrgGr \n", + "405 i0 1704410170 4 33 grrrgrrrgrrrrrgGG \n", + "406 i6 1704410200 0 24 grrrgGGGrgrrgrrr \n", + "407 i6 1704410200 1 19 grrrgGGGrgrrgGGr \n", + "408 i6 1704410200 2 39 grrrgrrrrgrrgGGG \n", + "409 i6 1704410200 3 65 gGGGgrrrrgrrgrrr \n", + "410 i6 1704410200 4 23 grrrgrrrrgGGgrrr \n", "\n", - "[422 rows x 11 columns]" + " start_dt \n", + "399 2024-01-05 08:15:30 \n", + "400 2024-01-05 08:15:30 \n", + "401 2024-01-05 08:16:10 \n", + "402 2024-01-05 08:16:10 \n", + "403 2024-01-05 08:16:10 \n", + "404 2024-01-05 08:16:10 \n", + "405 2024-01-05 08:16:10 \n", + "406 2024-01-05 08:16:40 \n", + "407 2024-01-05 08:16:40 \n", + "408 2024-01-05 08:16:40 \n", + "409 2024-01-05 08:16:40 \n", + "410 2024-01-05 08:16:40 " ] }, - "execution_count": 15, "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sigtable" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "i0 grrrgrrrgrrrrrgrr\n", - "i1 grrrrrrgrr\n", - "i2 rrggrrr\n", - "i3 grrrrgrrrrgrrrrgrrrr\n", - "i6 grrrgrrrrgrrgrrr\n", - "i7 rrrggrr\n", - "i8 grrrrrrrgrrrgrrr\n", - "i9 rrrr\n" - ] + "output_type": "display_data" } ], "source": [ - "node2init = {}\n", - "for node in nodes:\n", - " node_id = node.getID()\n", - " conns = [(c.getJunctionIndex(), c) for c in node.getConnections()]\n", - " conns = [c for c in conns if c[0] >= 0]\n", - " conns = sorted(conns, key=lambda x: x[0])\n", - " state = []\n", - " for i, ci in conns:\n", - " if ci.getTLLinkIndex() < 0:\n", - " continue\n", - " are_foes = False\n", - " for j, cj in conns:\n", - " if ci.getTo() == cj.getTo():\n", - " continue\n", - " if node.areFoes(i, j):\n", - " are_foes = True\n", - " break\n", - " state.append('r' if are_foes else 'g')\n", - " node2init[node_id] = state\n", - "\n", - "# 어떤 연결과도 상충이 일어나지는 않지만, 신호가 부여되어 있는 경우에는 r을 부여\n", - "for _, row in sigtable.iterrows():\n", - " node_id = row['node_id']\n", + "sigtable = histid.copy()\n", + "sigtable['init_state'] = sigtable['node_id'].map(node2init)\n", + "for i, row in sigtable.iterrows():\n", + " node_id = row.node_id\n", " inc_edge_A = row.inc_edge_A\n", " inc_edge_B = row.inc_edge_B\n", " out_edge_A = row.out_edge_A\n", " out_edge_B = row.out_edge_B\n", - " move_A = row['move_A']\n", - " move_B = row['move_B']\n", + " state = copy.deepcopy(node2init)[node_id]\n", "\n", " if pd.isna(inc_edge_A) or pd.isna(out_edge_A):\n", " continue\n", @@ -593,7 +630,8 @@ " for conn in inc_edge_A.getConnections(out_edge_A):\n", " index = conn.getTLLinkIndex()\n", " if index >= 0:\n", - " node2init[node_id][index] = 'r'\n", + " state[index] = 'G'\n", + " sigtable.at[i, 'state'] = ''.join(state)\n", "\n", " if pd.isna(inc_edge_B) or pd.isna(out_edge_B):\n", " continue\n", @@ -603,401 +641,722 @@ " for conn in inc_edge_B.getConnections(out_edge_B):\n", " index = conn.getTLLinkIndex()\n", " if index >= 0:\n", - " node2init[node_id][index] = 'r'\n", - "for node_id in node_ids:\n", - " print(node_id, \"\".join(node2init[node_id]))" + " state[index] = 'G'\n", + " sigtable.at[i, 'state'] = ''.join(state)\n", + "sigtable = sigtable.dropna(subset='state')\n", + "sigtable = sigtable.reset_index(drop=True)\n", + "sigtable['phase_sumo'] = sigtable.groupby(['node_id', 'start_unix']).cumcount()\n", + "sigtable = sigtable[['node_id', 'start_unix', 'phase_sumo', 'duration', 'state']]\n", + "sigtable = sigtable.sort_values(by=['start_unix', 'node_id'])\n", + "sigtable_dt = sigtable.copy()[sigtable.start_unix >= fmins[m]-360]\n", + "sigtable_dt['start_dt'] = sigtable_dt['start_unix'].apply(lambda x:datetime.fromtimestamp(x))\n", + "sigtable_dt = sigtable_dt[sigtable_dt.node_id!='i9']\n", + "sigtable_dt = sigtable_dt[sigtable_dt.node_id!='i2']\n", + "display(sigtable_dt)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "['g', 'r', 'r', 'r', 'g', 'r', 'r', 'r', 'r', 'g', 'r', 'r', 'g', 'r', 'r', 'r']\n", - "['G', 'r', 'r', 'r', 'g', 'r', 'r', 'r', 'r', 'g', 'r', 'r', 'g', 'r', 'r', 'r']\n", - "['g', 'r', 'r', 'r', 'g', 'r', 'r', 'r', 'r', 'g', 'r', 'r', 'g', 'r', 'r', 'r']\n" + "3380\n", + "2024-01-05 08:15:00\n", + "2024-01-05 08:20:00\n", + "2024-01-05 08:17:00\n", + "2024-01-05 07:20:40\n" ] } ], "source": [ - "print(node2init['i6'])\n", - "state = copy.deepcopy(node2init)['i6']\n", - "state[0] = 'G'\n", - "print(state)\n", - "print(node2init['i6'])" + "print(sigtable.start_unix.max() - sigtable.start_unix.min())\n", + "print(datetime.fromtimestamp(fmins[m - 1]))\n", + "print(datetime.fromtimestamp(fmins[m]))\n", + "print(datetime.fromtimestamp(sigtable.start_unix.max()))\n", + "print(datetime.fromtimestamp(sigtable.start_unix.min()))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "sigtable1 = sigtable.copy()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['i0', 'i1', 'i2', 'i3', 'i6', 'i7', 'i8', 'i9']" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted(sigtable.node_id.unique())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "strings = ['\\n']\n", + "for key, group in sigtable.groupby(['start_unix', 'node_id']):\n", + " start_unix = key[0]\n", + " node_id = key[1]\n", + " strings.append(f' \\n')\n", + " for i, row in group.iterrows():\n", + " duration = row.duration\n", + " state = row.state\n", + " strings.append(f' \\n')\n", + " strings.append(' \\n')\n" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2024-01-05 07:20:00\n" - ] - }, - { - "data": { - "text/html": [ - "
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node_idstart_unixphase_sumodurationstate
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9i31704403220323grrrrgGGGGgrrrrgrrrr
10i21704403230036GGggGGG
11i21704403230120rrggrrr
12i21704403230226rrggGGG
13i11704403250037gGGGGGrgrr
14i11704403250173grrGGGGgrr
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16i01704403270037gGGrgrrrgGGGGrgrr
17i01704403270139grrGgrrrgrrrrGgrr
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19i01704403270330grrrgGGrgrrrrrgGr
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22i71704403320126GGrggGG
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" - ], - "text/plain": [ - " node_id start_unix phase_sumo duration state\n", - "1 i6 1704403210 0 24 grrrgGGGrgrrgrrr\n", - "2 i6 1704403210 1 19 grrrgGGGrgrrgGGr\n", - "3 i6 1704403210 2 29 grrrgrrrrgrrgGGG\n", - "4 i6 1704403210 3 56 gGGGgrrrrgrrgrrr\n", - "5 i6 1704403210 4 22 grrrgrrrrgGGgrrr\n", - "0 i9 1704403210 0 39 GGGG\n", - "6 i3 1704403220 0 38 gGGGrgrrrrgGGGrgrrrr\n", - "7 i3 1704403220 1 39 grrrGgrrrrgrrrGgrrrr\n", - "8 i3 1704403220 2 40 grrrrgrrrrgrrrrgGGGG\n", - "9 i3 1704403220 3 23 grrrrgGGGGgrrrrgrrrr\n", - "10 i2 1704403230 0 36 GGggGGG\n", - "11 i2 1704403230 1 20 rrggrrr\n", - "12 i2 1704403230 2 26 rrggGGG\n", - "13 i1 1704403250 0 37 gGGGGGrgrr\n", - "14 i1 1704403250 1 73 grrGGGGgrr\n", - "15 i1 1704403250 2 40 grrrrrrgGG\n", - "16 i0 1704403270 0 37 gGGrgrrrgGGGGrgrr\n", - "17 i0 1704403270 1 39 grrGgrrrgrrrrGgrr\n", - "18 i0 1704403270 2 25 grrrgGGGgrrrrrgrr\n", - "19 i0 1704403270 3 30 grrrgGGrgrrrrrgGr\n", - "20 i0 1704403270 4 29 grrrgrrrgrrrrrgGG\n", - "21 i7 1704403320 0 33 GGrggGG\n", - "22 i7 1704403320 1 26 GGrggGG\n", - "23 i9 1704403350 0 39 GGGG\n", - "24 i3 1704403360 0 38 gGGGrgrrrrgGGGrgrrrr\n", - "25 i3 1704403360 1 39 grrrGgrrrrgrrrGgrrrr\n", - "26 i3 1704403360 2 40 grrrrgrrrrgrrrrgGGGG\n", - "27 i3 1704403360 3 23 grrrrgGGGGgrrrrgrrrr\n", - "28 i6 1704403360 0 24 grrrgGGGrgrrgrrr\n", - "29 i6 1704403360 1 19 grrrgGGGrgrrgGGr" - ] - }, - "metadata": {}, - "output_type": "display_data" + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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row.node_id\n", - " inc_edge_A = row.inc_edge_A\n", - " inc_edge_B = row.inc_edge_B\n", - " out_edge_A = row.out_edge_A\n", - " out_edge_B = row.out_edge_B\n", - " state = copy.deepcopy(node2init)[node_id]\n", - "\n", - " if pd.isna(inc_edge_A) or pd.isna(out_edge_A):\n", - " continue\n", - " else:\n", - " inc_edge_A = net.getEdge(inc_edge_A)\n", - " out_edge_A = net.getEdge(out_edge_A)\n", - " for conn in inc_edge_A.getConnections(out_edge_A):\n", - " index = conn.getTLLinkIndex()\n", - " if index >= 0:\n", - " state[index] = 'G'\n", - " sigtable.at[i, 'state'] = ''.join(state)\n", - "\n", - " if pd.isna(inc_edge_B) or pd.isna(out_edge_B):\n", - " continue\n", - " else:\n", - " inc_edge_B = net.getEdge(inc_edge_B)\n", - " out_edge_B = net.getEdge(out_edge_B)\n", - " for conn in inc_edge_B.getConnections(out_edge_B):\n", - " index = conn.getTLLinkIndex()\n", - " if index >= 0:\n", - " state[index] = 'G'\n", - " sigtable.at[i, 'state'] = ''.join(state)\n", - "sigtable = sigtable.dropna(subset='state')\n", - "sigtable = sigtable.reset_index(drop=True)\n", - "sigtable['phase_sumo'] = sigtable.groupby(['node_id', 'start_unix']).cumcount()\n", - "sigtable = sigtable[['node_id', 'start_unix', 'phase_sumo', 'duration', 'state']]\n", - "sigtable = sigtable.sort_values(by=['start_unix', 'node_id'])\n", - "print(datetime.fromtimestamp(fmins[m]))\n", - "display(sigtable[:30])" + "strings = ['\\n']\n", + "for key, group in sigtable.groupby(['start_unix', 'node_id']):\n", + " start_unix = key[0]\n", + " node_id = key[1]\n", + " strings.append(f' \\n')\n", + " for i, row in group.iterrows():\n", + " duration = row.duration\n", + " state = row.state\n", + " strings.append(f' \\n')\n", + " strings.append(' \\n')\n", + "strings.append('')\n", + "strings = ''.join(strings)\n", + "# 저장\n", + "path_output = '../../Data/networks/SN_sample.tll.xml'\n", + "with open(path_output, 'w') as f:\n", + " f.write(strings)\n", + "print(strings)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1621,55 +1980,6 @@ " f.write(strings)\n", "print(strings)" ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: 'one_cycle.csv'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[9], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[0;32m 2\u001b[0m path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mone_cycle.csv\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m----> 3\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 5\u001b[0m \u001b[38;5;66;03m# 적당히 지정하세요\u001b[39;00m\n\u001b[0;32m 6\u001b[0m node_id \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnode_id\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m0\u001b[39m]\n", - "File \u001b[1;32mc:\\Github\\SNITS_RealTimeSignals\\rts\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:912\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m 899\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m 900\u001b[0m dialect,\n\u001b[0;32m 901\u001b[0m delimiter,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 908\u001b[0m dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[0;32m 909\u001b[0m )\n\u001b[0;32m 910\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m--> 912\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Github\\SNITS_RealTimeSignals\\rts\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:577\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 574\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m 576\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 577\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 579\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m 580\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", - "File \u001b[1;32mc:\\Github\\SNITS_RealTimeSignals\\rts\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:1407\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 1404\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 1406\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1407\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Github\\SNITS_RealTimeSignals\\rts\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:1661\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m 1659\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[0;32m 1660\u001b[0m mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1661\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1662\u001b[0m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1663\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1664\u001b[0m \u001b[43m 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\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1670\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1671\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 1672\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n", - "File \u001b[1;32mc:\\Github\\SNITS_RealTimeSignals\\rts\\lib\\site-packages\\pandas\\io\\common.py:859\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m 854\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m 855\u001b[0m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m 856\u001b[0m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m 857\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[0;32m 858\u001b[0m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[1;32m--> 859\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[0;32m 860\u001b[0m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 861\u001b[0m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 862\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 863\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 864\u001b[0m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 865\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 866\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 867\u001b[0m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[0;32m 868\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n", - "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'one_cycle.csv'" - ] - } - ], - "source": [ - "import pandas as pd\n", - "path = 'one_cycle.csv'\n", - "df = pd.read_csv(path)\n", - "\n", - "# 적당히 지정하세요\n", - "node_id = df['node_id'][0]\n", - "offset = 999\n", - "\n", - "# xml양식대로 작성\n", - "strings = ['\\n']\n", - "strings.append(f' \\n')\n", - "for i, row in df.iterrows():\n", - " dur = row['dura']\n", - " state = row['signal']\n", - " strings.append(f' \\n')\n", - "strings.append(' \\n')\n", - "strings.append('')\n", - "strings = ''.join(strings)\n", - "print(strings)\n", - "\n", - "# 저장\n", - "path_output = 'example.tll.xml'\n", - "with open(path_output, 'w') as f:\n", - " f.write(strings)" - ] } ], "metadata": { diff --git a/Analysis/0119_test_2/example.add.xml b/Analysis/0119_test_2/example.add.xml new file mode 100644 index 000000000..974bac024 --- /dev/null +++ b/Analysis/0119_test_2/example.add.xml @@ -0,0 +1,24 @@ + + + + + + + + + + \ No newline at end of file diff --git a/Analysis/0119_test_2/example.net.xml b/Analysis/0119_test_2/example.net.xml new file mode 100644 index 000000000..5db7310f1 --- /dev/null +++ b/Analysis/0119_test_2/example.net.xml @@ -0,0 +1,186 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/Analysis/0119_test_2/example.rou.xml b/Analysis/0119_test_2/example.rou.xml new file mode 100644 index 000000000..56b8f3985 --- /dev/null +++ b/Analysis/0119_test_2/example.rou.xml @@ -0,0 +1,17 @@ + + + + + + + + + + + + + + + + + diff --git a/Analysis/0119_test_2/example.sumocfg b/Analysis/0119_test_2/example.sumocfg new file mode 100644 index 000000000..b317b170b --- /dev/null +++ b/Analysis/0119_test_2/example.sumocfg @@ -0,0 +1,7 @@ + + + + + + + diff --git a/Data/networks/SN_sample.rou.xml b/Data/networks/SN_sample.rou.xml new file mode 100644 index 000000000..279eb08b5 --- /dev/null +++ b/Data/networks/SN_sample.rou.xml @@ -0,0 +1,19 @@ + + + + + + + + + + + + + + + + + + + diff --git a/Data/networks/SN_sample.sumocfg b/Data/networks/SN_sample.sumocfg deleted file mode 100644 index 036d77192..000000000 --- a/Data/networks/SN_sample.sumocfg +++ /dev/null @@ -1,14 +0,0 @@ - - - - - - - - - - - - - diff --git a/Data/networks/SN_sample.xml.rou.xml b/Data/networks/SN_sample.xml.rou.xml deleted file mode 100644 index 33813e9a5..000000000 --- a/Data/networks/SN_sample.xml.rou.xml +++ /dev/null @@ -1,16 +0,0 @@ - - - - - - - - - - - - - - - diff --git a/Data/networks/SN_sample_with_a_route.net.xml b/Data/networks/SN_sample_with_a_route.net.xml deleted file mode 100644 index ed6bd1e4d..000000000 --- a/Data/networks/SN_sample_with_a_route.net.xml +++ /dev/null @@ -1,2218 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/Data/networks/example/example.add.xml b/Data/networks/example/example.add.xml new file mode 100644 index 000000000..375348ebd --- /dev/null +++ b/Data/networks/example/example.add.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/Data/networks/example/example.net.xml b/Data/networks/example/example.net.xml new file mode 100644 index 000000000..5db7310f1 --- /dev/null +++ b/Data/networks/example/example.net.xml @@ -0,0 +1,186 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/Data/networks/example/example.rou.xml b/Data/networks/example/example.rou.xml new file mode 100644 index 000000000..56b8f3985 --- /dev/null +++ b/Data/networks/example/example.rou.xml @@ -0,0 +1,17 @@ + + + + + + + + + + + + + + + + + diff --git a/Data/tables/0110_view_tables.ipynb b/Data/tables/0110_view_tables.ipynb index e8411f15e..2c041b4ae 100644 --- a/Data/tables/0110_view_tables.ipynb +++ b/Data/tables/0110_view_tables.ipynb @@ -35,14 +35,14 @@ }, { "attachments": { - "image.png": { - "image/png": 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" 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" 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